{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "year=2019\n",
    "month=6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('../py')\n",
    "import db\n",
    "import weighted\n",
    "import inspect\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_original=pd.read_sql(sql=f\"select * from _{year}{month:02} where monthly_salary>0 and monthly_salary<80000 and YEAR(publish_date)={year} and MONTH(publish_date)={month}\", con=db.get_conn())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(83048, 94)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_original.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data_original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "del data['publish_date']\n",
    "del data['published_on_weekend']\n",
    "del data['title']\n",
    "del data['company_title']\n",
    "del data['company_description']\n",
    "del data['job_description']\n",
    "del data['job_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sub_stats_by_prefix(data, prefix):\n",
    "    \n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    salary_mean=[]\n",
    "    salary_median=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    count=[]\n",
    "    \n",
    "    features_out=[]\n",
    "    for feature in features:\n",
    "        #print(feature)\n",
    "        idata=data[data[feature]==1]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(weighted.weighted_mean(values, weights))\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        features_out.append(feature)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data['rank']=range(0,len(features_out))\n",
    "    sub_data[prefix]=[f.replace(prefix,'') for f in features_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "    sub_data['rank']=range(1,len(features_out)+1)\n",
    "    #sub_data=sub_data.reset_index()\n",
    "    return sub_data\n",
    "\n",
    "def apply_style(sub_data):\n",
    "    return sub_data.style.hide_index().format(\n",
    "        {\"percentage\":\"{:.2%}\",\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"salary_95_min\":\"{:.0f}\",\"salary_95_max\":\"{:.0f}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pl=get_sub_stats_by_prefix(data,'pl_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"col_heading level0 col0\" >rank</th> \n",
       "        <th class=\"col_heading level0 col1\" >pl_</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col3\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col4\" >salary_95_min</th> \n",
       "        <th class=\"col_heading level0 col5\" >salary_95_max</th> \n",
       "        <th class=\"col_heading level0 col6\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col7\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow0_col1\" class=\"data row0 col1\" >julia</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow0_col2\" class=\"data row0 col2\" >37500</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow0_col3\" class=\"data row0 col3\" >37500</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow0_col4\" class=\"data row0 col4\" >37500</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow0_col5\" class=\"data row0 col5\" >37500</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow0_col6\" class=\"data row0 col6\" >1</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow0_col7\" class=\"data row0 col7\" >0.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow1_col1\" class=\"data row1 col1\" >haskell</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow1_col2\" class=\"data row1 col2\" >26222</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow1_col3\" class=\"data row1 col3\" >25000</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow1_col4\" class=\"data row1 col4\" >7768</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow1_col5\" class=\"data row1 col5\" >45000</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow1_col6\" class=\"data row1 col6\" >45</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow1_col7\" class=\"data row1 col7\" >0.01%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow2_col1\" class=\"data row2 col1\" >rust</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow2_col2\" class=\"data row2 col2\" >18687</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow2_col3\" class=\"data row2 col3\" >15000</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow2_col4\" class=\"data row2 col4\" >5000</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow2_col5\" class=\"data row2 col5\" >60000</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow2_col6\" class=\"data row2 col6\" >248</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow2_col7\" class=\"data row2 col7\" >0.07%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow3_col1\" class=\"data row3 col1\" >python</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow3_col2\" class=\"data row3 col2\" >17982</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow3_col3\" class=\"data row3 col3\" >16000</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow3_col4\" class=\"data row3 col4\" >3750</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow3_col5\" class=\"data row3 col5\" >45000</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow3_col6\" class=\"data row3 col6\" >27847</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow3_col7\" class=\"data row3 col7\" >7.60%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow4_col1\" class=\"data row4 col1\" >go</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow4_col2\" class=\"data row4 col2\" >17612</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow4_col3\" class=\"data row4 col3\" >15000</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow4_col4\" class=\"data row4 col4\" >5250</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow4_col5\" class=\"data row4 col5\" >40000</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow4_col6\" class=\"data row4 col6\" >25515</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow4_col7\" class=\"data row4 col7\" >6.96%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow5_col0\" class=\"data row5 col0\" >6</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow5_col1\" class=\"data row5 col1\" >perl</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow5_col2\" class=\"data row5 col2\" >17604</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow5_col3\" class=\"data row5 col3\" >17500</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow5_col4\" class=\"data row5 col4\" >5250</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow5_col5\" class=\"data row5 col5\" >40000</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow5_col6\" class=\"data row5 col6\" >2971</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow5_col7\" class=\"data row5 col7\" >0.81%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow6_col0\" class=\"data row6 col0\" >7</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow6_col1\" class=\"data row6 col1\" >matlab</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow6_col2\" class=\"data row6 col2\" >17435</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow6_col3\" class=\"data row6 col3\" >16666</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow6_col4\" class=\"data row6 col4\" >5000</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow6_col5\" class=\"data row6 col5\" >37500</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow6_col6\" class=\"data row6 col6\" >5196</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow6_col7\" class=\"data row6 col7\" >1.42%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow7_col0\" class=\"data row7 col0\" >8</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow7_col1\" class=\"data row7 col1\" >lua</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow7_col2\" class=\"data row7 col2\" >16902</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow7_col3\" class=\"data row7 col3\" >15000</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow7_col4\" class=\"data row7 col4\" >3750</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow7_col5\" class=\"data row7 col5\" >45000</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow7_col6\" class=\"data row7 col6\" >2698</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow7_col7\" class=\"data row7 col7\" >0.74%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow8_col0\" class=\"data row8 col0\" >9</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow8_col1\" class=\"data row8 col1\" >ruby</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow8_col2\" class=\"data row8 col2\" >16200</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow8_col3\" class=\"data row8 col3\" >15000</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow8_col4\" class=\"data row8 col4\" >2500</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow8_col5\" class=\"data row8 col5\" >34538</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow8_col6\" class=\"data row8 col6\" >1091</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow8_col7\" class=\"data row8 col7\" >0.30%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow9_col0\" class=\"data row9 col0\" >10</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow9_col1\" class=\"data row9 col1\" >cpp</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow9_col2\" class=\"data row9 col2\" >15349</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow9_col3\" class=\"data row9 col3\" >12500</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow9_col4\" class=\"data row9 col4\" >3750</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow9_col5\" class=\"data row9 col5\" >37500</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow9_col6\" class=\"data row9 col6\" >59314</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow9_col7\" class=\"data row9 col7\" >16.18%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow10_col0\" class=\"data row10 col0\" >11</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow10_col1\" class=\"data row10 col1\" >kotlin</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow10_col2\" class=\"data row10 col2\" >15319</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow10_col3\" class=\"data row10 col3\" >12500</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow10_col4\" class=\"data row10 col4\" >5686</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow10_col5\" class=\"data row10 col5\" >32792</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow10_col6\" class=\"data row10 col6\" >819</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow10_col7\" class=\"data row10 col7\" >0.22%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow11_col0\" class=\"data row11 col0\" >12</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow11_col1\" class=\"data row11 col1\" >swift</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow11_col2\" class=\"data row11 col2\" >14576</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow11_col3\" class=\"data row11 col3\" >12500</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow11_col4\" class=\"data row11 col4\" >5000</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow11_col5\" class=\"data row11 col5\" >35000</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow11_col6\" class=\"data row11 col6\" >2799</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow11_col7\" class=\"data row11 col7\" >0.76%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow12_col0\" class=\"data row12 col0\" >13</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow12_col1\" class=\"data row12 col1\" >objective_c</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow12_col2\" class=\"data row12 col2\" >14168</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow12_col3\" class=\"data row12 col3\" >12500</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow12_col4\" class=\"data row12 col4\" >6259</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow12_col5\" class=\"data row12 col5\" >32350</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow12_col6\" class=\"data row12 col6\" >393</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow12_col7\" class=\"data row12 col7\" >0.11%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow13_col0\" class=\"data row13 col0\" >14</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow13_col1\" class=\"data row13 col1\" >typescript</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow13_col2\" class=\"data row13 col2\" >13542</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow13_col3\" class=\"data row13 col3\" >12500</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow13_col4\" class=\"data row13 col4\" >5500</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow13_col5\" class=\"data row13 col5\" >29166</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow13_col6\" class=\"data row13 col6\" >915</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow13_col7\" class=\"data row13 col7\" >0.25%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow14_col0\" class=\"data row14 col0\" >15</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow14_col1\" class=\"data row14 col1\" >java</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow14_col2\" class=\"data row14 col2\" >13258</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow14_col3\" class=\"data row14 col3\" >12500</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow14_col4\" class=\"data row14 col4\" >3750</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow14_col5\" class=\"data row14 col5\" >32500</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow14_col6\" class=\"data row14 col6\" >122634</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow14_col7\" class=\"data row14 col7\" >33.46%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow15_col0\" class=\"data row15 col0\" >16</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow15_col1\" class=\"data row15 col1\" >php</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow15_col2\" class=\"data row15 col2\" >12990</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow15_col3\" class=\"data row15 col3\" >11500</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow15_col4\" class=\"data row15 col4\" >3502</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow15_col5\" class=\"data row15 col5\" >36814</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow15_col6\" class=\"data row15 col6\" >17022</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow15_col7\" class=\"data row15 col7\" >4.64%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow16_col0\" class=\"data row16 col0\" >17</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow16_col1\" class=\"data row16 col1\" >javascript</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow16_col2\" class=\"data row16 col2\" >11472</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow16_col3\" class=\"data row16 col3\" >11000</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow16_col4\" class=\"data row16 col4\" >3750</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow16_col5\" class=\"data row16 col5\" >24000</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow16_col6\" class=\"data row16 col6\" >47572</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow16_col7\" class=\"data row16 col7\" >12.98%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow17_col0\" class=\"data row17 col0\" >18</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow17_col1\" class=\"data row17 col1\" >vba</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow17_col2\" class=\"data row17 col2\" >11265</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow17_col3\" class=\"data row17 col3\" >10750</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow17_col4\" class=\"data row17 col4\" >4650</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow17_col5\" class=\"data row17 col5\" >24700</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow17_col6\" class=\"data row17 col6\" >392</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow17_col7\" class=\"data row17 col7\" >0.11%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow18_col0\" class=\"data row18 col0\" >19</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow18_col1\" class=\"data row18 col1\" >c_sharp</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow18_col2\" class=\"data row18 col2\" >11107</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow18_col3\" class=\"data row18 col3\" >10000</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow18_col4\" class=\"data row18 col4\" >3750</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow18_col5\" class=\"data row18 col5\" >25000</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow18_col6\" class=\"data row18 col6\" >47690</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow18_col7\" class=\"data row18 col7\" >13.01%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow19_col0\" class=\"data row19 col0\" >20</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow19_col1\" class=\"data row19 col1\" >delphi</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow19_col2\" class=\"data row19 col2\" >10586</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow19_col3\" class=\"data row19 col3\" >10000</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow19_col4\" class=\"data row19 col4\" >3750</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow19_col5\" class=\"data row19 col5\" >22500</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow19_col6\" class=\"data row19 col6\" >1313</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow19_col7\" class=\"data row19 col7\" >0.36%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow20_col0\" class=\"data row20 col0\" >21</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow20_col1\" class=\"data row20 col1\" >visual_basic</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow20_col2\" class=\"data row20 col2\" >9491</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow20_col3\" class=\"data row20 col3\" >9000</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow20_col4\" class=\"data row20 col4\" >4500</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow20_col5\" class=\"data row20 col5\" >18917</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow20_col6\" class=\"data row20 col6\" >68</td> \n",
       "        <td id=\"T_bcd6f292_9c8b_11e9_a175_701ce71031efrow20_col7\" class=\"data row20 col7\" >0.02%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x22d51e16c88>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"col_heading level0 col0\" >rank</th> \n",
       "        <th class=\"col_heading level0 col1\" >pl_</th> \n",
       "        <th class=\"col_heading level0 col2\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow0_col1\" class=\"data row0 col1\" >java</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow0_col2\" class=\"data row0 col2\" >33.46%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow1_col1\" class=\"data row1 col1\" >cpp</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow1_col2\" class=\"data row1 col2\" >16.18%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow2_col1\" class=\"data row2 col1\" >c_sharp</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow2_col2\" class=\"data row2 col2\" >13.01%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow3_col1\" class=\"data row3 col1\" >javascript</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow3_col2\" class=\"data row3 col2\" >12.98%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow4_col1\" class=\"data row4 col1\" >python</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow4_col2\" class=\"data row4 col2\" >7.60%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow5_col0\" class=\"data row5 col0\" >6</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow5_col1\" class=\"data row5 col1\" >go</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow5_col2\" class=\"data row5 col2\" >6.96%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow6_col0\" class=\"data row6 col0\" >7</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow6_col1\" class=\"data row6 col1\" >php</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow6_col2\" class=\"data row6 col2\" >4.64%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow7_col0\" class=\"data row7 col0\" >8</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow7_col1\" class=\"data row7 col1\" >matlab</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow7_col2\" class=\"data row7 col2\" >1.42%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow8_col0\" class=\"data row8 col0\" >9</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow8_col1\" class=\"data row8 col1\" >perl</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow8_col2\" class=\"data row8 col2\" >0.81%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow9_col0\" class=\"data row9 col0\" >10</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow9_col1\" class=\"data row9 col1\" >swift</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow9_col2\" class=\"data row9 col2\" >0.76%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow10_col0\" class=\"data row10 col0\" >11</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow10_col1\" class=\"data row10 col1\" >lua</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow10_col2\" class=\"data row10 col2\" >0.74%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow11_col0\" class=\"data row11 col0\" >12</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow11_col1\" class=\"data row11 col1\" >delphi</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow11_col2\" class=\"data row11 col2\" >0.36%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow12_col0\" class=\"data row12 col0\" >13</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow12_col1\" class=\"data row12 col1\" >ruby</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow12_col2\" class=\"data row12 col2\" >0.30%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow13_col0\" class=\"data row13 col0\" >14</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow13_col1\" class=\"data row13 col1\" >typescript</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow13_col2\" class=\"data row13 col2\" >0.25%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow14_col0\" class=\"data row14 col0\" >15</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow14_col1\" class=\"data row14 col1\" >kotlin</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow14_col2\" class=\"data row14 col2\" >0.22%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow15_col0\" class=\"data row15 col0\" >16</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow15_col1\" class=\"data row15 col1\" >objective_c</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow15_col2\" class=\"data row15 col2\" >0.11%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow16_col0\" class=\"data row16 col0\" >17</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow16_col1\" class=\"data row16 col1\" >vba</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow16_col2\" class=\"data row16 col2\" >0.11%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow17_col0\" class=\"data row17 col0\" >18</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow17_col1\" class=\"data row17 col1\" >rust</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow17_col2\" class=\"data row17 col2\" >0.07%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow18_col0\" class=\"data row18 col0\" >19</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow18_col1\" class=\"data row18 col1\" >visual_basic</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow18_col2\" class=\"data row18 col2\" >0.02%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow19_col0\" class=\"data row19 col0\" >20</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow19_col1\" class=\"data row19 col1\" >haskell</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow19_col2\" class=\"data row19 col2\" >0.01%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow20_col0\" class=\"data row20 col0\" >21</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow20_col1\" class=\"data row20 col1\" >julia</td> \n",
       "        <td id=\"T_bff2b34c_9c8b_11e9_88cb_701ce71031efrow20_col2\" class=\"data row20 col2\" >0.00%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x22d54ee04a8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=data_pl[['pl_','percentage']].sort_values(by='percentage', ascending=False)\n",
    "data_pl['rank']=range(1,22)\n",
    "data_pl=data_pl[['rank','pl_','percentage']]\n",
    "#data_pl.style.format({\"percentage\":\"{:.2%}\"})\n",
    "data_pl.style.hide_index().format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_pl=data_pl.sort_values(by='percentage')\n",
    "\n",
    "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n",
    "labels = data_pl['pl_']\n",
    "sizes = data_pl['percentage']\n",
    "#explode = (0, 0.1, 0, 0)  # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize=(15, 9))\n",
    "ax1.pie(sizes, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.title(\"Programming Languages in China, March 2019\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word Cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "wc=WordCloud()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "wc_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in data_pl.iterrows():\n",
    "    wc_dict[row['pl_']]=row['percentage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "wc=wc.generate_from_frequencies(wc_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "plt.imshow(wc, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
